Research Article Open Access

Fuzzy Time Series: An Application to Tourism Demand Forecasting

Muhammad Hisyam Lee1, Maria Elena Nor1, Suhartono2, Hossain Javedani Sadaei1, Nur Haizum Abd Rahman1 and Nur Arina Bazilah Kamisan1
  • 1 Deparment of Mathematics, Faculty of Science, University Technology, Malaysia
  • 2 Department of Statistics, Faculty of Mathematics and Natural Sciences, Institute Technology Sepuluh Nopember, Indonesia


Problem statement: Forecasting is very important in many types of organizations since predictions of future events must be incorporated into the decision-making process. In the case of tourism demand, better forecast would help directors and investors make operational, tactical and strategic decisions. Besides that, government bodies need accurate tourism demand forecasts to plan required tourism infrastructures, such as accommodation site planning and transportation development, among other needs. There are many types of forecasting methods. Generally, time series forecasting can be divided into classical method and modern methods. Recent studies show that the newer and more advanced forecasting techniques tend to result in improved forecast accuracy, but no clear evidence shows that any one model can consistently outperform other models in the forecasting competition. Approach: In this study, the performance of forecasting between classical methods (Box-Jenkins methods Seasonal Auto-Regressive Integrated Moving Average (SARIMA), Holt Winters and time series regression) and modern methods (fuzzy time series) has been compared by using data of tourist arrivals to Bali and Soekarno-Hatta gate in Indonesia as case study. Results: The empirical results show that modern methods give more accurate forecasts compare to classical methods. Chen’s fuzzy time series method outperforms all the classical methods and others more advance fuzzy time series methods. We also found that the performance of fuzzy time series methods can be improve by using transformed data. Conclusion: It is found that the best method to forecast the tourist arrivals to Bali and Soekarno-Hatta was to be the FTS i.e., method after using data transformation. Although this method known to be the simplest or conventional methods of FTS, yet this result should not be odd since several previous studies also have shown that simple method could outperform more advance or complicated methods.

American Journal of Applied Sciences
Volume 9 No. 1, 2012, 132-140


Submitted On: 25 October 2011 Published On: 24 November 2011

How to Cite: Lee, M. H., Nor, M. E., Suhartono, Sadaei, H. J., Rahman, N. H. A. & Kamisan, N. A. B. (2012). Fuzzy Time Series: An Application to Tourism Demand Forecasting. American Journal of Applied Sciences, 9(1), 132-140.

  • 11 Citations



  • Holt winters
  • time series regression
  • classical methods
  • forecast accuracy
  • competition models
  • consistently outperform
  • transformed data
  • tourist arrivals
  • forecasting competition